TrustToken, a Trusted SoC solution for Non-Trusted Intellectual Property (IP)s
Muhammed Kawser Ahmed, Sujan Kumar Saha, Christophe Bobda

TL;DR
TrustToken is a low-overhead, root-of-trust security solution for non-trusted IPs in heterogeneous SoCs, enabling secure communication and execution against hardware and software threats.
Contribution
It introduces a novel TrustToken architecture that provides secure key generation, trusted communication, and low resource overhead for protecting non-trusted IPs in SoCs.
Findings
TrustToken achieves low resource utilization with minimal LUT, FF, and BUFG overhead.
It effectively enforces IP authorization and secure communication in SoC environments.
The solution demonstrates low-cost, secure, and trusted execution for non-trusted IPs.
Abstract
Secure and trustworthy execution in heterogeneous SoCs is a major priority in the modern computing system. Security of SoCs mainly addresses two broad layers of trust issues: 1. Protection against hardware security threats(Side-channel, IP Privacy, Cloning, Fault Injection, and Denial of Service); and 2. Protection against malicious software attacks running on SoC processors. To resist malicious software-level attackers from gaining unauthorized access and compromising security, we propose a root of trust-based trusted execution mechanism \textbf{\textit{(named as \textbf{TrustToken}) }}. TrustToken builds a security block to provide a root of trust-based IP security: secure key generation and truly random source. \textbf{TrustToken} only allows trusted communication between the non-trusted third-party IP and the rest of the SoC world by providing essential security features, i.e.,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPhysical Unclonable Functions (PUFs) and Hardware Security · Security and Verification in Computing · Adversarial Robustness in Machine Learning
